An Efficient Technique for Frequent Itemset Generation Using the Significance Degree of Items

Mining association rules is one of the most important tasks in data mining. The classical model of association rules mining is supportconfidence. The support-confidence model concentrates only on the existence or absence of an item in transaction records and does not take into account the products’ prices and quantities and how such these detailed information can affect the overall performance of rule mining process. In this paper a new measure for mining association rules is proposed based on the quantity of each itemset bought in a transaction; which is the significant degree measure to improve the classical method of mining association rules. The property of the new interestingness measures is analyzed, which validity has been tested in this paper.